Bab 05 Manajemen Data Dan Pengetahuan

Data and Knowledge
Management
Chapter 05
By : Darwin, S.Kom., M.Kom., CPS®, CRSP, CH,

BKP, CDM, Google Ads Certified, Google My Business Certified
PRODI. TEKNIK INFORMATIKA (S-1)

Overview
• Describe how important are data and data management to
organizations; from confidential customer information, to intellectual
property, to financial transactions, to social media posts,
• organizations possess massive amounts of data that are critical to
organizational success and that they need to manage
• Managing data is critically important in large organizations, and
equally important to small organizations too.
Objectives
After completing this unit, you’ll be able to:
• Discuss ways that common challenges in managing data can be
addressed using data governance.
• Define Big Data, and discuss its basic characteristics.
• Explain how to interpret the relationships depicted in an entity-
relationship diagram.
• Discuss the advantages and disadvantages of relational databases.
• Explain the elements necessary to successfully implement and
maintain data warehouses.
• Describe the benefi ts and challenges of implementing knowledge
management systems in organizations.
Contents
1. Managing Data
2. Big Data
3. The Database Approach
4. Database Management Systems
5. Data Warehouses and Data Marts
6. Knowledge Management
Managing Data
Data and Knowledge Management [5.1]

Managing Data
• The Difficulties of Managing Data
• Data Governance
Multiple Sources of Data
• Internal Sources
- Corporate databases, company
documents
• Personal Sources
- Personal thoughts, opinions, experiences
• External Sources
- Commercial databases, government
reports, and corporate Web sites.
Data Governance
• An approach to managing information
across an entire organization.
• Master Data
• Master Data Management
Big Data
Data and Knowledge Management[5.2]

Big Data
• Defining Big Data
• Characteristics of Big Data
• Managing Big Data
• Leveraging Big Data
Defining Big Data
• Big data is difficult to define
• Two Descriptions of Big Data
From Gartner Research (Big
Data Description 1 of 2)
• Diverse, high-volume, high-velocity
information assets that require new forms
of processing to enable enhanced decision
making, insight discovery, and process
optimization. (www.gartner.com)
From the Bid Data Institute (Big
Data Description 2 of 2)
• Exhibit variety
• Includes structured, unstructured, and semi-structured
data
• Are generated at high velocity with an uncertain pattern
• Do not fit neatly into traditional, structured, relational
databases
• Can be captured, processed, transformed, and analyzed
in a reasonable amount of time only by sophisticated
information systems.
• (www.the-bigdatainstitute.com)
Defining Big Data
• Big Data Generally Consist of:
- Traditional enterprise data
- Machine-generated/sensor data
- Social data
- Images captured by billions of devices
located around the world
 Digital cameras, camera phones, medical
scanners, and security cameras
Characteristics of Big Data
• Volume
• Velocity
• Variety
Managing Big Data
• When properly analyzed big data can reveal
valuable patterns and information.
• Database environment
• Traditional relational databases versus
NoSQL databases
• Open source solutions
Leveraging Big Data
• Creating Transparency
• Enabling Experimentation
• Segmenting Population to Customize Actions
• Replacing/Supporting Human Decision
Making with Automated Algorithms
• Innovating New Business Models, Products,
and Services
• Organizations Can Analyze Far More Data
The Database Approach

The Database Approach
• The Data Hierarchy
• Designing the Database
Databases Minimize Three Main
Problems
• Data Redundancy
• Data Isolation
• Data Inconsistency
Databases Maximize the
Following
• Data Security
• Data Integrity
• Data Independence
Hierarchy of data for a computer-based file
Source: Introduction to Information Systems, Supporting and Transforming Business (Rainer, Prince, Cegielski) Fifth Edition, Chapter 05
Data Hierarchy
• Bit
• Byte
• Field
• Data File or Table
• Database
Designing the Database
• Key Terms
- Data Model
- Entity
- Instance
- Attribute
- Primary Key
- Secondary Keys
Designing the Database
• Entity-Relationship Modeling
• Entity-Relationship Diagram
• Cardinality
• Modality
Database Management
Systems

Database Management Systems
• The Relational Database Model
• Databases in Action
The Relational Database Model
• Based on the concept of two-dimensional
tables
• Database Management System (DBMS)
• Query Languages
• Data Dictionary
• Normalization
Data Warehouses and
Data Marts

Data Warehouses and Data
Marts
• Describing Data Warehouses and Data
Marts
• A Generic Data Warehouse Environment
Describing Data Warehouses & Data Marts
• Data Warehouse
- A repository of historical data that are
organized by subject to support decision
makers in the organization
• Data Mart
- A low-cost, scaled-down version of a data
warehouse designed for end-user needs in
a strategic business unit (SBU) or
individual department.
Describing Data Warehouses & Data Marts
• Basic characteristics of data warehouses

and data marts
- Organized by business dimension or subject
- Use online analytical processing (OLAP)
- Integrated
- Time variant
- Nonvolatile
- Multidimensional
A Generic Data Warehouse
Environment
• Source Systems
 Data Integration
 Storing the Data
• Metadata
• Data Quality
• Data Governance
• Users
Data Warehouses and Data
Marts
Knowledge
Management

Knowledge Management
• Concepts and Definitions
• Knowledge Management Systems
• The KMS Cycle
Concepts & Definitions
• Knowledge Management (KM)
- A process that helps manipulate
important knowledge that comprises part
of the organization’s memory, usually in
an unstructured format.
• Knowledge
• Explicit & Tacit Knowledge
• Knowledge Management System (KMS)
Knowledge Management
Systems (KMS)
• Refer to the use of modern information
technologies – the Internet, intranet,
extranets, databases – to systematize,
enhance, and expedite intrafirm and
interfirm knowledge management.
Best practices
The KMS Cycle
• Create Knowledge
• Capture Knowledge
• Refine Knowledge
• Store Knowledge
• Manage Knowledge
• Disseminate Knowledge
The knowledge management system cycle
Lesson Summary
You should now be able to:
• Discuss ways that common challenges in managing data can be
addressed using data governance
• Deﬁne Big Data, and discuss its basic characteristics
• Explain how to interpret the relationships depicted in an entity-
relationship diagram
• Discuss the advantages and disadvantages of relational databases
• Explain the elements necessary to successfully implement and
maintain data warehouses
• Describe the beneﬁ ts and challenges of implementing knowledge
management systems in organizations
Unit Summary
You should now be able to:
• Describe multiple sources of data
• Describe strategy for implementing data governance
• Define three distinct characteristics of Big Data
• Describe hierarchy of data
• Describe data warehouse and data marts
• Identify the advantages and disadvantages of relational databases
• Describe Concepts & Definitions of Knowledge Management Systems
• Identify KMS Cylce
Question & Answers

Bab 05 Manajemen Data Dan Pengetahuan

Uploaded by

Copyright:

Available Formats

Bab 05 Manajemen Data Dan Pengetahuan

Uploaded by

Document Information

Original Title

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Bab 05 Manajemen Data Dan Pengetahuan

Uploaded by

Copyright:

Available Formats

Data and Knowledge

By : Darwin, S.Kom., M.Kom., CPS®, CRSP, CH,

PRODI. TEKNIK INFORMATIKA (S-1)

PRODI. TEKNIK INFORMATIKA (S-1)

PRODI. TEKNIK INFORMATIKA (S-1)

PRODI. TEKNIK INFORMATIKA (S-1)

PRODI. TEKNIK INFORMATIKA (S-1)

PRODI. TEKNIK INFORMATIKA (S-1)

• Basic characteristics of data warehouses

PRODI. TEKNIK INFORMATIKA (S-1)

You might also like